Information recommendation method, computer device, and storage medium

ABSTRACT

Information recommendation methods are provided. Image information corresponding to an image is obtained by processing circuitry. The image is associated with a user identifier. A user tag set corresponding to the user identifier and the image information is generated. A feature vector corresponding to user tags in the user tag set and the image information is formed. The feature vector is processed according to a trained information recommendation model, to obtain a recommendation parameter of to-be-recommended information. A recommendation of the to-be-recommended information is provided to a terminal corresponding to the user identifier according to the recommendation parameter.

RELATED APPLICATION

This application is a continuation of International Application No.PCT/CN2018/100235, filed on Aug. 13, 2018, which claims priority toChinese Patent Application No. 201710806375.0, filed on Sep. 8, 2017,and entitled “INFORMATION RECOMMENDATION METHOD AND APPARATUS, COMPUTERDEVICE, AND STORAGE MEDIUM”. The prior applications are herebyincorporated by reference in their entirety.

FIELD OF THE TECHNOLOGY

This application relates to the field of computer processing, and inparticular, to an information recommendation method, a computer device,and a storage medium.

BACKGROUND OF THE DISCLOSURE

With the development of the Internet, lives of people are increasinglyclosely linked with the Internet. In this fast-paced era, users expectto find required products through the Internet quickly. However, massiveproduct data is constantly generated on the Internet every day. Thismakes it difficult for an Internet user to quickly find requiredinformation or information of interest. To help users obtain informationand content conveniently, related information is recommended to theusers in many fields. However, in a conventional recommendation mode,the same information is usually recommended to all users. hisinformation recommendation mode is not accurate enough, and easilydisturbs other users.

SUMMARY

According to embodiments of this application, information recommendationmethods, information processing apparatuses, and non-transitorycomputer-readable storage mediums are provided.

In an embodiment, an information recommendation method is provided.Image information corresponding to an image is obtained. The image isassociated with a user identifier. A user tag set corresponding to theuser identifier and the image information is generated. A feature vectorcorresponding to user tags in the user tag set and the image informationis formed. The feature vector is processed according to a trainedinformation recommendation model, to obtain a recommendation parameterof to-be-recommended information. A recommendation of theto-be-recommended information is provided to a terminal corresponding tothe user identifier according to the recommendation parameter.

In an embodiment, the image information includes image contentinformation and image acquisition information. The image contentinformation includes a plurality of images. The images are classifiedaccording to the image content information, and a first user tag setcorresponding to the image content information is determined based on aresult of the classification according to the image content information.The images are classified according to the image acquisitioninformation, and a second user tag set corresponding to the imageacquisition information is determined based on a result of theclassification according to the image acquisition information.

In an embodiment, matching with standard user models is performedaccording to the user tags in the user tag set and the image informationcorresponding to the user tags in a case that a scale of the user tagset corresponding to the user identifier is less than a preset scale. Atarget standard user model of the standard user models matching the useridentifier is determined. A standard user feature vector correspondingto the target standard user model is obtained as the feature vectorcorresponding to the user identifier.

In an embodiment, degrees of matching between a user corresponding tothe user identifier and the standard user models are calculatedaccording to the user tags in the user tag set and the image informationcorresponding to the user tags. A standard user model of the standarduser models with a highest degree of matching of the calculated degreesof matching is selected as the target standard user model matching theuser identifier.

In an embodiment, image quantities corresponding to the user tags in theuser tag set are obtained. Current scores corresponding to the user tagsare determined according to the image quantities. For each of thestandard user models, standard scores corresponding to standard usertags that are in the respective standard user model and that are thesame as the user tags are obtained; degrees of similarity between theuser tags in the user tag set and the standard user tags are calculatedaccording to the standard scores and the corresponding current scores;and the degree of matching between the user corresponding to the useridentifier and the respective standard user model is obtained accordingto the degrees of similarity.

In an embodiment, training image information is obtained. A traininguser tag set is generated according to the training image information. Atraining feature vector is formed according to training user tags in thetraining user tag set and the training image information correspondingto the training user tag set. A standard output result corresponding tothe training feature vector is obtained. Model training is performed byusing the training feature vector and the standard output result as atraining sample, to obtain a target information recommendation model.

In an embodiment, a primary user tag set corresponding to the useridentifier and the image information is determined. A secondary user tagset is generated based on extracted features of the primary user tagset. The user tag set corresponding to the user identifier is formedaccording to the primary user tag set and the secondary user tag set.

In an embodiment, each piece of the to-be-recommended information has acorresponding information recommendation model. The feature vector isprocessed according to the corresponding information recommendationmodels, to obtain a corresponding recommendation parameter set. Eachrecommendation parameter in the recommendation parameter set is used fordetermining a recommendation probability of one piece of theto-be-recommended information. An information recommendation listcorresponding to the user identifier is generated according to therecommendation probabilities corresponding to the pieces of theto-be-recommended information. Target to-be-recommended informationcorresponding to the user identifier is determined according to theinformation recommendation list.

In an embodiment, the to-be-recommended information is provided to theterminal corresponding to the user identifier in a form of a picturewhen the recommendation parameter is greater than a preset threshold.

In an embodiment, there is provided an information recommendationmethod, in which image information corresponding to an image isobtained. The image is associated with a user identifier. A current usertag set corresponding to the user identifier and the image informationis generated. To-be-recommended information and an expected user tag setcorresponding to the to-be-recommended information are obtained. Adegree of similarity between the current user tag set and the expecteduser tag set is calculated. A recommendation of the to-be-recommendedinformation is provided to a terminal corresponding to the useridentifier according to the degree of similarity.

In an embodiment, a primary user tag set corresponding to the useridentifier and the image information is generated. A secondary user tagset is generated based on extracted features of the primary user tagset. The current user tag set is generated based on the primary user tagset and the secondary user tag set. Further, the degree of similarity iscalculated between the secondary user tag set and the expected user tagset.

In an embodiment, image quantities corresponding to current user tags inthe current user tag set are obtained. Current scores corresponding tothe current user tags are determined according to the image quantities.An expected score corresponding to each user tag in the expected usertag set is obtained. The degree of similarity between the current usertag set and the expected user tag set is calculated according to thecurrent scores and the expected scores.

Embodiments of the present disclosure further includes informationprocessing apparatuses configured to, and non-transitorycomputer-readable mediums storing instructions which when executed byone or more processors cause the one or more processors to, perform oneor a combination of the above methods.

Details of one or more embodiments of this application are proposed inthe following accompanying drawings and description. Other features,objectives and advantages of this application will become more evidentfrom the specification, the accompanying drawings, and the claims.

BRIEF DESCRIPTION OF THE DRAWINGS

To describe the technical solutions in the embodiments of thisapplication more clearly, the following briefly introduces theaccompanying drawings required for describing the embodiments. While theaccompanying drawings in the following description show some embodimentsof this application, a person of ordinary skill in the art may stillderive other drawings from these accompanying drawings.

FIG. 1 is a diagram of an application environment of an informationrecommendation method in an embodiment;

FIG. 2 is a flowchart of an information recommendation method in anembodiment;

FIG. 3 is a flowchart of a method for generating a user tag setcorresponding to a user identifier according to image information in anembodiment;

FIG. 4A is a schematic diagram of an interface of a content-basedclassification result in an embodiment;

FIG. 4B is a schematic diagram of an interface of a holiday-basedclassification result in an embodiment;

FIG. 5 is a flowchart of a method for forming a feature vectorcorresponding to a user identifier according to user tags in a user tagset and corresponding image information in an embodiment;

FIG. 6 is a flowchart of a method for determining a target standard usermodel matching a user identifier in an embodiment;

FIG. 7 is a flowchart of a method for calculating degrees of matchingbetween a user corresponding to a user identifier and standard usermodels in an embodiment;

FIG. 8 is a flowchart of a method for establishing an informationrecommendation model in an embodiment;

FIG. 9 is a flowchart of a method for generating a user tag setcorresponding to a user identifier according to image information in anembodiment;

FIG. 10 is a flowchart of a method for inputting a feature vector into atrained information recommendation model and outputting a recommendationparameter of to-be-recommended information in an embodiment;

FIG. 11 is a flowchart of an information recommendation method inanother embodiment;

FIG. 12 is a flowchart of an information recommendation method in stillanother embodiment;

FIG. 13 is a schematic flowchart of obtaining a secondary user tagaccording to image information in an embodiment;

FIG. 14 is a flowchart of a method for calculating a degree ofsimilarity between a current user tag set and an expected user tag setin an embodiment;

FIG. 15 is a flowchart of an information recommendation method inanother embodiment;

FIG. 16 is a structural block diagram of an information recommendationapparatus in an embodiment;

FIG. 17 is a structural block diagram of a forming module in anembodiment;

FIG. 18 is a structural block diagram of an information recommendationapparatus in another embodiment;

FIG. 19 is a structural block diagram of an information recommendationapparatus in still another embodiment; and

FIG. 20 is a schematic diagram of an internal structure of a computerdevice in an embodiment.

DESCRIPTION OF EMBODIMENTS

To make the objectives, technical solutions and advantages of thisapplication more comprehensible, the following describes thisapplication in further detail with reference to the accompanyingdrawings and embodiments. It is to be understood that the embodimentsdescribed herein are merely for the illustration of this application,and are not intended to limit this application.

As shown in FIG. 1, in an embodiment, an information recommendationmethod may be applied in an application environment shown in FIG. 1. Inthe application environment, a terminal 102 is connected to a server 104through a network. The terminal 102 may be a smartphone, a tabletcomputer, a notebook computer, a desktop computer, and the like, but isnot limited thereto. The server 104 may be an independent server, or maybe a server cluster formed by multiple servers. The server 104 firstobtains image information from the terminal 102, the image informationhaving a corresponding user identifier, generates a user tag setcorresponding to the user identifier according to the image information,forms a feature vector corresponding to the user identifier according touser tags in the user tag set and the corresponding image information,inputs the feature vector into a trained information recommendationmodel, outputs a recommendation parameter of to-be-recommendedinformation, and recommends the to-be-recommended information to theterminal 102 corresponding to the user identifier according to therecommendation parameter.

As shown in FIG. 2, in an embodiment, an information recommendationmethod is proposed. The method applied to a server is used as an examplein the following. The method specifically includes the following steps:

In step S202, Image information is obtained. The image information has acorresponding user identifier. Further, a user tag set corresponding tothe user identifier is generated according to the image information.

The image information includes at least one of image contentinformation, image acquisition information, and image quantityinformation. The image may be a picture or a video. The video may beconsidered as consisting of, or including, frames of pictures. Theobtained image information corresponds to a user identifier. The useridentifier is used for uniquely identifying a user. The user identifiermay be an account registered by the user, a terminal identifier, aunique number allocated to the user, or the like. The user tag setincludes multiple user tags, and the user tags represent features of theuser. The user tag may be one or more of an age, a gender, a hobby, afinancial capability, and a schedule of the user. In an embodiment, theimage information may be information obtained by a terminal byrecognizing an image. For example, the terminal first recognizes imagecontent by using an image recognition technology, and then uploads therecognized image content to the server. The server obtains imageinformation, and performs classification according to the obtained imageinformation. For example, the server uniformly classifies a recognizedmountain, sea, snowfield, sky, and the like into a scenery class, anduniformly classifies images including two or more characters into agroup photo class. Then, a user tag set corresponding to the useridentifier is generated according to a classification result. Forexample, user tags include scenery, group photo, selfie, food, and thelike. In another embodiment, the image information may be an imageitself. The terminal directly uploads images in the terminal to theserver. Then, the server recognizes the images by using a photorecognition technology, performs classification according to arecognition result, and then generates a user tag set corresponding tothe user identifier according to a classification result.

In step S204, a feature vector corresponding to the user identifier isformed according to user tags in the user tag set and the correspondingimage information.

Each user tag in the user tag set represents one user feature. Theserver determines a feature value corresponding to each user tagaccording to each user tag and the corresponding image information, andforms a feature vector corresponding to the user identifier according tothe feature values corresponding to the user tags. The feature vectorrefers to a vector that can comprehensively reflect features of theuser. For example, it is assumed that the user tags in the user tag setincludes 6 user tags, for example, food, scenery, group photo, selfie,animal, and building. A picture quantity corresponding to each user tagis used as a corresponding feature value. For example, there are 5 foodpictures, 10 scenery pictures, 20 group photos, 15 selfies, 3 animalpictures, and 18 building pictures. An arrangement order of the usertags is set in advance. For example, the arrangement order is: food,scenery, group photo, selfie, animal, and building. Then, thecorresponding feature vector may be represented as (5, 10, 20, 15, 3,18).

In step S206, the feature vector is processed according to a trainedinformation recommendation model, to obtain a recommendation parameterof to-be-recommended information.

The information recommendation model is used for predicting whether auser is interested in the to-be-recommended information, and outputtinga recommendation parameter corresponding to the to-be-recommendedinformation. The recommendation parameter reflects interest of the userin the to-be-recommended information. The to-be-recommended informationrefers to target information needing to be pushed to the user. In anembodiment, the recommendation parameter is an output result of whetherto recommend the to-be-recommended information or not, that is, therecommendation parameter is “recommending” or “not recommending”.Subsequently, the server can directly determine, according to therecommendation result, whether to recommend the to-be-recommendedinformation. In another embodiment, the recommendation parameter is arecommendation probability or non-recommendation probability obtainedthrough calculation, that is, a probability that the user is interestedin the to-be-recommended information is obtained. Subsequently, it isdetermined, according to the recommendation probability, whether torecommend the to-be-recommended information. The informationrecommendation model may be stored in the server performing theinformation recommendation method, or may be stored in another server.When the recommendation parameter needs to be obtained, the informationrecommendation model stored in the other server is invoked to processthe feature vector, to obtain the recommendation parameter of theto-be-recommended information.

In step S208, the to-be-recommended information is recommended to aterminal corresponding to the user identifier according to therecommendation parameter.

The recommendation parameter reflects interest of the user in theto-be-recommended information. If the recommendation parameter is arecommendation probability, a recommendation threshold is preset. If therecommendation parameter is greater than the preset recommendationthreshold, it indicates that the user is interested in theto-be-recommended information, and the server pushes theto-be-recommended information to the terminal corresponding to the useridentifier. If the recommendation parameter is a “recommending” or “notrecommending” result, it is directly determined, according to the“recommending” or “not recommending” result, whether to push theto-be-recommended information to the terminal corresponding to the useridentifier. That is, if the recommendation parameter is “recommending”,the to-be-recommended information is pushed to the terminalcorresponding to the user identifier; if the recommendation parameter is“not recommending”, the to-be-recommended information is not pushed tothe terminal corresponding to the user identifier.

In the foregoing information recommendation method, image information isobtained; a user tag set corresponding to a user identifier is generatedaccording to the image information; then, a feature vector correspondingto the user identifier is formed according to user tags in the user tagset and the corresponding image information; the feature vector is inputto a trained information recommendation model, and a recommendationparameter of to-be-recommended information is output; and theto-be-recommended information is recommended to a terminal correspondingto the user identifier according to the recommendation parameter.According to the information recommendation method, a user tag setcapable of reflecting features of a user can be established by usingimage information, and a recommendation parameter of the user forrecommended information is predicted according to the user tag set byusing a trained information recommendation model. Then, the informationis recommended according to the recommendation parameter, thereby notonly improving the accuracy of recommendation but also avoidingdisturbing users not interested in the information.

As shown in FIG. 3, in an embodiment, the image information includesimage content information and image acquisition information; the step ofgenerating a user tag set corresponding to the user identifier accordingto the image information includes the following steps:

In step S202A, images are classified according to the image contentinformation, and a user tag set corresponding to the content informationis determined.

The image information includes image content information and imageacquisition information. The image content information refers to contentinformation included in an image. Images are classified according tocontent information included in the images. For example, pictures may beclassified as scenery, food, baby, group photo, selfie, and the likeaccording to the content information. In an embodiment, the pictureswith image content information of scenery, such as a mountain, a sea, asnowfield, a sky, and forests, are uniformly classified into a sceneryclass; pictures containing small babies are uniformly classified into ababy class; selfie pictures obtained through a terminal are uniformlyclassified into a selfie class; and the like. FIG. 4A is a schematicdiagram of an interface of a content-based classification result in anembodiment. Then, a user tag corresponding to each class of contentinformation is determined according to a classification result, forforming a user tag set based on content classes. For example, it isassumed that classification is performed according to content of imageinformation, and an obtained classification result includes scenery,group photo, food, and selfie. In this case, the scenery, group photo,food, and selfie are used as corresponding user tags respectively. Inother words, a user tag set obtained after classification based onrecognized content is used as the user tag set corresponding to thecontent information.

In step S202B, the images are classified according to the imageacquisition information, and a user tag set corresponding to the imageacquisition information is determined.

The image acquisition information includes at least one of acquisitiontime information, acquisition location information, and the like. Theserver may classify images according to acquisition time only, orclassify images according to acquisition locations only, or classifyimages according to both the acquisition time and the acquisitionlocations. In an embodiment, images are classified according toacquisition locations (photographing locations). First, acquisitionlocation information corresponding to each image is obtained, to obtaina city where each image is taken. Then, the number of times or frequencyof appearance of each city is counted, and a city with a highest numberof times or frequency is used as a place of residence of the user.Places other than the place of residence are used as traveldestinations. Images taken at the same location may be classified intoone class. In this way, a city where the user is located can beobtained, and the number of travelling times can be counted, includingthe number of times of domestic travel and the number of times ofoverseas travel. All these can be used as user tags. In anotherembodiment, images are classified according to acquisition time. Forexample, if photos are taken on holiday or a specific festival, thephotos may be classified into a Spring Festival album, a National Dayalbum, and the like. FIG. 4B is a schematic diagram of an interface of aholiday-based classification result in an embodiment. A holiday albummay also be a tag of the user, reflecting a daily life state and aphotographing preference of the user. For example, if the user takes allphotos during holidays, it indicates that the user is a typical officeworker. A user tag set obtained by classifying images according to theimage acquisition information is used as the user tag set correspondingto the image information. In another embodiment, the image acquisitioninformation may further include acquisition device information, and thecorresponding acquisition device information is also used as a user tag.For example, if an acquisition device is IPhone7, the IPhone7 is alsoused as a user tag.

As shown in FIG. 5, in an embodiment, step S204 of forming a featurevector corresponding to the user identifier according to user tags inthe user tag set and the corresponding image information includes thefollowing steps:

In step S204A, a determination is made as to whether a scale of the usertag set corresponding to the user identifier is less than a presetscale; if yes, step S204B is performed; otherwise, 5204D is performeddirectly.

The scale of the user tag set is used for measuring the integrity ofuser tags, including at least one of the quantity of user tags, and atotal quantity of images corresponding to the user tag set. User tagsets corresponding to some user identifiers have relatively smallscales. For example, some users just changed their phones, and thereforeonly a few photos are stored in the phones. In this case, few user tagscan be obtained merely according to the existing images, and aprediction result obtained from a small quantity of user tags is easilydistorted. Therefore, it is necessary to establish an auxiliary tagsystem for the users with incomplete tag information, to make tags ofthe users complete. First, a preset scale of a user tag set isconfigured in advance. The preset scale may be a preset user tagquantity. For example, a preset user tag quantity of 10 is used as thepreset scale. The preset scale may also be a preset total imagequantity. For example, a preset image quantity of 50 is used as thepreset scale. The preset scale may include both the preset user tagquantity and the total image quantity. That is, the preset scaleincludes the preset user tag quantity and the total image quantity, andthe preset scale is achieved only when the two conditions are met at thesame time. Specifically, it is first determined, according to the scaleof the user tag set corresponding to the user identifier, whether thepreset scale is achieved; if the preset scale is achieved, it indicatesthat the user tag set corresponding to the user identifier is relativelycomplete, and the feature vector corresponding to the user identifiercan be directly formed according to user tags in the existing user tagset and the corresponding image information. If the preset scale is notachieved, it indicates that the user tag set corresponding to the useridentifier needs to be further improved.

In step S204B, matching with preset standard user models is performedaccording to the existing user tags in the user tag set and thecorresponding image information, and a target standard user modelmatching the user identifier is determined.

The standard user model refers to a preset user model that can representfeatures of a group, for example, a standard young mother modelrepresenting the group of young moms. Users with the same or similarfeatures are classified into the same group, and a standard user modelthat can represent features of the group is set. The standard user modelis described through a user tag set that can reflect features of thecorresponding group. Specifically, multiple standard user models are setin advance. For example, a standard young mother model, a standard youngboy model, and a standard anime-and-manga girl model are set. When thescale of the user tag set corresponding to the user identifier is lessthan the preset scale, the server obtains existing user tags in the usertag set, performs matching with user tag sets corresponding to thecorresponding standard user models according to the existing user tags,and uses a standard user model having a highest degree of matching withthe user identifier as the matched target standard user model. In anembodiment, a degree of matching may be calculated according to thequantity of identical user tags. For example, a user identifier of ato-be-improved user tag set corresponds to 6 existing user tags; it isassumed that there are 3 standard user models, which are A, B, and Crespectively. It is assumed that among user tags corresponding to themodel A, there are 4 user tags that are the same as the existing usertags; then, a corresponding degree of matching is 4; there are 3existing user tags that are the same as user tags of the model B, and acorresponding degree of matching is 3; there are 5 existing user tagsthat are the same as user tags of the model C, and a correspondingdegree of matching is 5. In this case, because the model C has thelargest quantity of user tags that are the same as those of the user,the model C is used as the matched target standard user model.

In step S204C, a standard user feature vector corresponding to thetarget standard user model is obtained, and the standard user featurevector is used as the feature vector corresponding to the useridentifier.

The standard user feature vector refers to a feature vectorcorresponding to the standard user model, and is determined according tothe user tag set corresponding to the standard user model and thecorresponding image information. Each user tag in the user tag setrepresents one user feature. According to image informationcorresponding to each user tag, a feature value corresponding to theuser tag is determined. A feature vector corresponding to the useridentifier is formed according to feature values corresponding to theuser tags corresponding to the standard user model. The standard userfeature vector can comprehensively reflect vectors of standard userfeatures. Specifically, after determining the target standard user modelmatching the user identifier, the server obtains a standard user featurevector corresponding to the target standard user model. The standarduser feature vector may be directly used as the feature vectorcorresponding to the user identifier, to facilitate subsequentcalculation of the recommendation parameter according to the featurevector. When the user tag set corresponding to the user identifier isnot complete enough, matching with preset standard user models isperformed, and a feature vector corresponding to a target standard usermodel obtained through matching is used as the feature vectorcorresponding to the user identifier, thereby improving the accuracy ofrecommendation.

In step 204D, the feature vector corresponding to the user identifier isformed according to the user tags in the user tag set and thecorresponding image information.

Specifically, when the scale of the user tag set is greater than thepreset scale, it indicates that the user tags in the user tag set arerelatively complete, and the server can directly determine the featurevector corresponding to the user identifier according to the user tagsin the user tag set and the corresponding image information. The featurevector refers to a vector that can reflect features of a usercomprehensively.

As shown in FIG. 6, in an embodiment, step S204B of performing matchingwith preset standard user models according to the existing user tags inthe user tag set and the corresponding image information, anddetermining a target standard user model matching the user identifierincludes the following steps:

In step S602, degrees of matching between a user corresponding to theuser identifier and the standard user models are calculated according tothe existing user tags in the user tag set and the corresponding imageinformation.

The degree of matching refers to a degree of matching between the usercorresponding to the user identifier and a standard user represented bythe standard user model. The existing user tag refers to a user tagalready obtained. Because the user tags in the user tag set are notcomplete enough, unknown (additional) user tags need to be obtainedaccording to the existing user tags. In order to obtain unknown usertags, preset standard user models need to be obtained first. A standarduser model represents a group having the same feature or similarfeatures, and the standard user model corresponds to a standard user tagset. By calculating the degrees of matching between the user and thestandard user models, a standard user model matching the user isdetermined, and a standard user tag set corresponding to the standarduser model is used as the user tag set of the user, thereby improvingthe user tag set of the user. Specifically, the server first determinesa current feature value corresponding to each user tag according to theexisting user tags and the corresponding image information, then obtainsstandard feature values corresponding to user tags that are in thestandard user models and that are the same as the existing user tags,and calculates degrees of matching between the user corresponding to theuser identifier and the standard user models according to the currentfeature values and the standard feature values.

In step S604, a standard user model with a highest degree of matchingobtained through calculation is used as the target standard user modelmatching the user identifier.

The target standard user model refers to a standard user model that isobtained through calculation and that matches the user tags.Specifically, after calculating the degrees of matching between the usercorresponding to the user identifier and the standard user models, theserver uses a standard user model, which is obtained throughcalculation, having a highest degree of matching with the usercorresponding to the user identifier as the target standard user model.By means of matching with standard user models, a standard user tag setcorresponding to a standard user model is used as the user tag setcorresponding to the user identifier, thereby helping improve theaccuracy of subsequently recommended information.

As shown in FIG. 7, in an embodiment, step S602 of calculating degreesof matching between a user corresponding to the user identifier and thestandard user models according to the existing user tags in the user tagset and the corresponding image information includes:

In step S602A, a first standard user model from the standard user modelsis obtained as a current to-be-matched standard user model.

Specifically, because there are multiple standard user models, a degreeof matching with each standard user model needs to be calculatedseparately. First, the server obtains one standard user model (the firststandard user model) from the multiple standard user models as a currentto-be-matched standard user model.

In step S602B, image quantities corresponding to the existing user tagsin the user tag set are obtained.

Specifically, an image quantity corresponding to an existing user tag isobtained from the image information corresponding to the existing usertag. The image quantity refers to the quantity of pictures. For example,it is assumed that the existing user tags include food, scenery, selfie,group photo, and the like. The image quantity corresponding to the foodtag is 7; the image quantity corresponding to the scenery tag is 5; theimage quantity corresponding to the selfie tag is 10; and the imagequantity corresponding to the group photo tag is 5.

In step S602C, current scores corresponding to the existing user tagsare determined according to quantity levels to which the imagequantities belong.

The current score refers to a score that corresponds to a user tag andthat is determined according to an image quantity corresponding to theuser tag. Specifically, a correspondence between image quantities andscores is set in advance. After obtaining an image quantitycorresponding to an existing user tag, the server determines acorresponding current score according to a quantity level to which theimage quantity belongs. For example, a tag scoring system is set. Whenan image quantity corresponding to a user tag is in a range of [1, 10),a current score corresponding to the user tag is set to 1; when theimage quantity is in the range of [10, 50), the current scorecorresponding to the user tag is set to 2; when the image quantity is inthe range of [50, 100), the current score corresponding to the user tagis set to 3; when the image quantity is in the range of [100, 200), thecurrent score corresponding to the user tag is set to 4; when the imagequantity is 200 or more, the current score corresponding to the user tagis set to 5. It is assumed that existing user tags of a user A includethe following three tags: food, game, and scenery, where there are 10food images, 5 game images, and 20 scenery images. In this case, currentscores corresponding to the existing user tags of the user A are asfollows: a score of 2 corresponding to the food tag, a score of 1corresponding to the game tag, and a score of 2 corresponding to thescenery tag.

In step S602D, standard scores corresponding to standard user tags thatare in the current to-be-matched standard user model and that are thesame as the existing user tags are obtained.

The standard score refers to a score corresponding to a standard usertag in a standard user model. First, standard user tags that are in thecurrent to-be-matched standard user model and that are the same as theexisting user tags are obtained, and then corresponding standard scoresare obtained. It is assumed that the existing user tags include food,game, and scenery. In this case, food, game and scenery tags in thestandard user model are obtained, and then standard scores correspondingto the food, game, and scenery are obtained. If a standard user tag thatis the same as the existing user tag does not exist in the standard usertags, a standard score of the corresponding standard user tag is 0. Forexample, there is no food user tag in the standard user tag setcorresponding to the standard user model, a standard score correspondingto the food tag in the standard user model is set to 0.

In step S602E, degrees of similarity between the existing user tags inthe user tag set and the standard user tags are obtained throughcalculation according to the standard scores and the correspondingcurrent scores.

The degree of similarity refers to a degree of similarity between a usertag corresponding to a current user and a standard user tag in astandard user model. The standard user tag refers to a tag that is in astandard user model and that is the same as an existing user tag. In anembodiment, a degree of similarity between the existing user tag and thestandard user tag is determined according to a ratio between scorescorresponding to the existing user tag and the standard user tag, thatis, determined according to a ratio between the current score and thestandard score. A greater one in the current score and the standardscore is used as a denominator, and a smaller one is used as anumerator. Then, a degree of similarity between the two is determined.For example, if a food tag corresponds to a current score of 2 and astandard score of 3, 2 is used as a numerator and 3 is used as adenominator to obtain that a degree of similarity between the two is ⅔.A degree of similarity between each existing user tag and each standarduser tag can be obtained through calculation in this manner.

In step S602F, a degree of matching between the user corresponding tothe user identifier and the current to-be-matched standard user model isobtained according to the degrees of similarity.

After the degree of similarity between each existing user tag and eachstandard user tag is obtained through calculation, a degree of matchingbetween the user corresponding to the user identifier and the currentto-be-matched standard user model is calculated according to the degreesof similarity. In an embodiment, the server may use a sum of the degreesof similarity between all the existing user tags and the standard usertags as a degree of matching between the user and the currentto-be-matched standard user model. For example, it is assumed thatexisting user tags corresponding to a user A include performance,scenery, screenshot, and beauty. As shown in Table 1, a current scorecorresponding to the performance is 3, a current score corresponding tothe scenery is 1, a current score corresponding to the screenshot is 2,and a current score corresponding to the beauty is 1. Scores of thecorresponding standard user tags in a standard user model 1 are asfollows: a standard score corresponding to the performance is 0, astandard score corresponding to the scenery is 2, a standard scorecorresponding to the screenshot is 4, and a standard score correspondingto the beauty is 1.

TABLE 1 User Performance Scenery Screenshot Beauty User A Score of 3Score of 1 Score of 2 Score of 1 Standard user model 1 Score of 0 Scoreof 2 Score of 4 Score of 1

First, degrees of similarity corresponding to tags are calculated. Adegree of similarity corresponding to the performance tag is 0, a degreeof similarity corresponding to the scenery tag is ½, a degree ofsimilarity corresponding to the screenshot tag is 2/4, a degree ofsimilarity corresponding to the beauty tag is 1. If a sum of the degreesof similarity is directly used as a degree of matching, the degree ofmatching=0+½+ 2/4+1=2. In another embodiment, the degree of matchingequals the total degree of similarity divided by a total user tagquantity, that is, the degree of matching=total degree of similarity/tagquantity.

In step S602G, a determination is made as to whether the currentto-be-matched standard user model is the last standard user model; ifyes, the process ends; otherwise, step S602H is performed.

Specifically, if the current to-be-matched standard user model is thelast standard user model, the process is ended. If the currentto-be-matched standard user model is not the last standard user model, anext standard user model is obtained continuously and used as thecurrent to-be-matched standard user model, and a degree of matchingbetween the user and the next standard user model is calculatedcontinuously.

In step S602H, a next standard user model is obtained as the currentto-be-matched standard user model, and steps S602D to 5602G areperformed repeatedly.

After calculating the degree of matching between the user and thecurrent standard user model, the server obtains a next standard usermodel as the current to-be-matched standard user model, and thenrepeatedly performs the step of obtaining standard scores correspondingto standard user tags that are in the current to-be-matched standarduser model and that are the same as the existing user tags, until thedegrees of matching between the user and the standard user models areobtained.

As shown in FIG. 8, before the step of processing the feature vectoraccording to a trained information recommendation model, to obtain arecommendation parameter of to-be-recommended information, the methodfurther includes: establishing an information recommendation model. Thestep of establishing an information recommendation model includes thefollowing steps:

In step S201A: training image information is obtained, and a traininguser tag set is generated according to the training image information.

The training image information refers to image information used astraining data of an information recommendation model. The training usertag set is obtained according to the training image information.Classification processing the same as that performed during theforegoing prediction is performed on the training image information toobtain a training user tag set. The training user tag set includesmultiple training user tags. The training user tags represent featuresof a user. The training user tag may be one or more of an age, a gender,a hobby, a financial capability, and a schedule of a training user. Theprocessing manner of obtaining training image information and generatinga training user tag set according to the training image informationduring establishment of an information recommendation model is keptconsistent with the processing manner of obtaining image information andgenerating a user tag set according to the image information before useof a trained information recommendation model, and extracted user tagsare also kept consistent.

In step S201B, a training feature vector is formed according to traininguser tags in the training user tag set and the corresponding imageinformation.

Each user tag in the training user tag set represents one user feature.According to image information corresponding to each training user tag,a training feature value corresponding to the training user tag isdetermined. A training feature vector is formed according to trainingfeature values corresponding to all the training user tags. The trainingfeature vector refers to a vector that can comprehensively reflectfeatures of a training user.

In step S201C, a standard output result corresponding to the trainingfeature vector is obtained.

The standard output result refers to a known result corresponding to thetraining feature vector. Image information with a known user behaviorresult is used as training data, and the corresponding known behaviorresult is used as a standard output result.

In step S201D, model training is performed by using the training featurevector and the corresponding standard output result as a trainingsample, to obtain a target information recommendation model.

The training sample is used for training the model, so as to learn aboutparameters of the model and obtain an information recommendation modelthrough training. Training is performed by using the training featurevector representing the features of the training user as an input of ato-be-trained information recommendation model, and using thecorresponding standard output result as an expected output. In thetraining process, model parameters of the information recommendationmodel are adjusted continuously, so that an actually outputrecommendation result is constantly closer to a standard output result,and training of the model is completed until an error between theactually output recommendation result and the standard output resultmeets a condition. Specifically, there are multiple methods for trainingthe information recommendation model, for example, multiple machinelearning models such as linear regression, a neural network, and asupport vector machine.

A specific training process is introduced below by using a trainingalgorithm of a linear regression model as an example. 1) Trainingfeatures are determined. Training features are determined according touser tags in a user tag set and corresponding image information. Forexample, it is set that there are 12 user features, including an age, agender, a total image quantity, a quantity of classified albums, aquantity of domestic travel albums, a quantity of overseas travelalbums, a quantity of holiday albums, a quantity of baby pictures, aquantity of scenery pictures, a quantity of selfie pictures, a quantityof group photo pictures, and a quantity of food pictures of the user. 2)A feature vector is formed according to the determined trainingfeatures. An arrangement order of the features in the feature vector isset. For example, it is set that feature vector=<age, gender, totalimage quantity, quantity of classified albums, quantity of domestictravel albums, quantity of overseas travel albums, quantity of holidayalbums, quantity of baby pictures, quantity of scenery pictures,quantity of selfie pictures, quantity of group photo pictures, quantityof food pictures>. 0 or 1 is used for representing the gender of male orfemale. For example, a 20-year-old male user has a total of 200pictures, 5 classified albums, 3 domestic travel albums, 1 overseastravel album, 1 holiday album, 0 baby pictures, 50 scenery pictures, 10selfie pictures, 100 group photo pictures, and 40 food pictures. Acorresponding feature vector may be represented as <20, 0, 200, 5, 3, 1,1, 0, 50, 10, 100, 40>0.3) User behavior data corresponding to thefeature vector is obtained. For example, it is labeled as 1 if theto-be-recommended information is clicked, and it is labeled as 0 if theto-be-recommended information is not clicked. 4) Training data isdetermined. A piece of complete training data is represented as <featurevector, label>0.5) A model training method based on training regressionis used. First, it is assumed that user features and behavior resultsmeet a linear relationship, and a corresponding formula is representedas follows: f ({right arrow over (x)}=w{right arrow over (x)}+b, whererepresents a feature vector, w represents a weight matrix, and brepresents an offset matrix. Linear regression means fitting an outputof the training data and a standard output by using a curve, so that anerror between the output of the training data and the standard output isas small as possible. Specifically, a loss function is defined asfollows:

${{J(w)} = {\frac{1}{2}{\sum\limits_{i = 1}^{n}\left( {{f\left( x_{i} \right)} - y_{i}} \right)^{2}}}},$

which represents a mean square error between an actual output value anda standard output value, where n represents n pieces of training data, f(x_(i)) represents an actual output of an i^(th) piece of training data,and y_(i) represents a standard output result corresponding to thei^(th) piece of training data. The objective is to minimize the lossfunction by adjusting the weight and the offset. A specific solutionmethod may be a gradient descent method, a least square method, or thelike.

As shown in FIG. 9, the step of generating a user tag set correspondingto the user identifier according to the image information includes thefollowing steps:

In step S202A, a primary user tag set corresponding to the useridentifier is determined according to the image information.

A primary user tag refers to a user tag that can be directly obtainedaccording to the image information. That is, user tags that can beobtained according to information of the image itself are referred to as“primary user tags”, and a tag set formed by the primary user tags isreferred to as a “primary user tag set”. The image information includesimage content information, image acquisition information, image quantityinformation, and the like. All user tags directly obtained according tothe image content information, the image acquisition information, andthe image quantity information are primary user tags. For example, usertags such as scenery, group photo, selfie, and food that are directlyobtained through classification according to the image contentinformation are all primary user tags.

In step S202B, features of the primary user tag set are extracted togenerate a corresponding secondary user tag set.

A secondary user tag refers to a tag indirectly obtained by extracting afeature of a primary user tag. Specifically, a mapping relationshipbetween primary user tags and secondary user tags are set in advance,where the primary user tags may be in a many-to-one relationship withthe secondary user tags. A corresponding secondary user tag isdetermined by extracting features corresponding to primary tags. Forexample, a corresponding secondary user tag is determined according tofeatures corresponding to three tags: “baby”, “gender”, and “age”,corresponding to the user. For example, it is assumed that a photoquantity (feature) corresponding to the “baby” tag is 100, a featurecorresponding to the gender tag is female, and a feature correspondingto the age tag is 25. Then, it can be inferred that the user is a youngmom, and correspondingly, “young mom” is used as a secondary tag of theuser. A social attribute of the user is extracted according to a groupphoto quantity (feature) corresponding to the “group photo” tagcorresponding to the user. If the group photo tag corresponds to a largequantity of photos, it can be inferred that the user is a “sociable”user, and “sociable” is used as a secondary tag of the user. A financialcapability of the user can be inferred according to a photographingmachine model and a photographing location of the user. For example, ifthe user corresponds to an ordinary photographing machine model and anordinary photographing location, it indicates that the user has a normalfinancial capability, and “normal financial capability” is used as acorresponding secondary user tag.

In step S202C, the user tag set corresponding to the user identifier isformed according to the primary user tag set and the secondary user tagset.

Specifically, in order to recommend information more desirably andaccurately, the obtained primary user tag set and secondary user tag setare both used as the user tag set corresponding to the user identifier.That is, the user tag set includes both primary user tags and secondaryuser tags. With the addition of the secondary user tags, it helpsfurther improve the accuracy of information recommendation.

As shown in FIG. 10, there are multiple pieces of to-be-recommendedinformation, and each piece of to-be-recommended information has acorresponding information recommendation model; step S208 of processingthe feature vector according to a trained information recommendationmodel, to obtain a recommendation parameter of to-be-recommendedinformation includes the following steps:

In step S208A, the feature vector is processed according to the trainedinformation recommendation models, to obtain a correspondingrecommendation parameter set, each recommendation parameter in therecommendation parameter set being used for determining a recommendationprobability of each piece of to-be-recommended information.

When there are multiple pieces of to-be-recommended information, eachpiece of to-be-recommended information has a corresponding informationrecommendation model, that is, there are multiple informationrecommendation models correspondingly. The feature vector is processedaccording to the trained information recommendation models, to obtain arecommendation parameter outputted by each information recommendationmodel, thereby forming a recommendation parameter set. Eachrecommendation parameter in the recommendation parameter set is used fordetermining a recommendation probability of each piece ofto-be-recommended information. Specifically, it is assumed that thereare N pieces of to-be-recommended information, and there are Ninformation recommendation models correspondingly; then, Nrecommendation parameters are obtained. A recommendation probability ofa corresponding piece of to-be-recommended information is determinedaccording to each recommendation parameter. That is, N recommendationprobabilities are determined according to N recommendation parameters.

In step S208B, an information recommendation list corresponding to theuser identifier is generated according to the recommendation probabilitycorresponding to each piece of to-be-recommended information.

Specifically, after determining the recommendation probabilitycorresponding to each piece of to-be-recommended information, the servergenerates an information recommendation list corresponding to the useridentifier according to values of the recommendation probabilities. Inan embodiment, according to the recommendation probabilitiescorresponding to the to-be-recommended information, theto-be-recommended information is sorted in descending order of therecommendation probabilities to generate the information recommendationlist.

In step S208C, target to-be-recommended information corresponding to theuser identifier is determined according to the informationrecommendation list.

Specifically, after determining the information recommendation list, theserver determines target to-be-recommended information corresponding tothe user identifier according to the information recommendation list. Inan embodiment, a preset quantity of first pieces of information (forexample, first three pieces of information) in the informationrecommendation list is used as the target to-be-recommended informationfor pushing. In another embodiment, to-be-recommended information whoserecommendation probability is greater than a preset probabilitythreshold (such as 60%) in the information recommendation list is usedas the target to-be-recommended information for pushing.

In an embodiment, the step of recommending the to-be-recommendedinformation to a terminal corresponding to the user identifier accordingto the recommendation parameter includes: pushing the to-be-recommendedinformation to the terminal corresponding to the user identifier in aform of a picture if the recommendation parameter is greater than apreset threshold.

A value of the recommendation parameter reflects a level of interest ofthe user in the to-be-recommended information. A recommendationparameter threshold is set in advance. When the recommendation parameteris greater than the preset threshold, the to-be-recommended informationis pushed to the terminal corresponding to the user identifier in a formof a picture. For example, during advertisement push, advertisementcontent is disguised as a photo and issued to a user interface, toattract attention of the user.

As shown in FIG. 11, an information recommendation method is proposed.The method includes the following steps:

In step S1101, training image information is obtained, and a traininguser tag set is generated according to the training image information.

In step S1102, a training feature vector is formed according to traininguser tags in the training user tag set and the corresponding imageinformation.

In step S1103, a standard output result corresponding to the trainingfeature vector is obtained.

In step S1104, model training is performed by using the training featurevector and the corresponding standard output result as a trainingsample, to obtain a target information recommendation model.

In step S1105, image information is obtained. The image information hasa corresponding user identifier, and includes image content informationand image acquisition information.

In step S1106, images are classified according to the image contentinformation, and a user tag set corresponding to the content informationis determined. Further, images are classified according to the imageacquisition information, and a user tag set corresponding to the imageacquisition information is determined.

In step S1107, a determination is made as to whether a scale of a usertag set corresponding to the user identifier is less than a presetscale; if yes, step S1108 is performed; otherwise, step S1109 isperformed.

In step S1108, matching with preset standard user models is performedaccording to existing user tags in the user tag set and thecorresponding image information, a target standard user model matchingthe user identifier is determined, a standard user feature vectorcorresponding to the target standard user model is obtained, and thestandard user feature vector is used as a feature vector correspondingto the user identifier.

In step S1109, a feature vector corresponding to the user identifier isformed by using user tags in the user tag set and the correspondingimage information.

In step S1110, the feature vector is processed according to the trainedinformation recommendation model, to obtain a recommendation parameterof to-be-recommended information.

In step S1111, the to-be-recommended information is recommended to aterminal corresponding to the user identifier according to therecommendation parameter.

As shown in FIG. 12, in an embodiment, an information recommendationmethod is proposed. The method includes the following steps:

In step S1202, image information is obtained. The image information hasa corresponding user identifier. A current user tag set corresponding tothe user identifier is generated according to the image information.

The image information includes at least one of image contentinformation, image acquisition information, and image quantityinformation. The image may be a picture or a video. The video may beconsidered as consisting of, or including, frames of pictures. Theobtained image information corresponds to a user identifier. The useridentifier is used for uniquely identifying a user. The user identifiermay be an account registered by the user, a terminal identifier, aunique number allocated to the user, or the like. The current user tagset includes multiple current user tags, and the current user tagsrepresent features of the current user. The current user tag may be anage, a gender, a hobby, a financial capability, a schedule, and the likeof the user. In an embodiment, the image information may be informationobtained by a terminal by recognizing an image. For example, theterminal first recognizes image content by using an image recognitiontechnology, and then uploads the recognized image content to a server.The server obtains image information, and performs classificationaccording to the obtained image information. For example, the serveruniformly classifies a recognized mountain, sea, snowfield, sky, and thelike into a scenery class, and uniformly classifies images including twoor more characters into a group photo class. Then, a current user tagset corresponding to the user identifier is generated according to aclassification result. For example, current user tags include scenery,group photo, selfie, food, and the like. In another embodiment, theimage information may be an image itself. The terminal directly uploadsimages in the terminal to the server. Then, the server recognizes theimages by using a photo recognition technology, performs classificationaccording to a recognition result, and then generates a current user tagset corresponding to the user identifier according to a classificationresult.

In step S1204, to-be-recommended information and an expected user tagset corresponding to the to-be-recommended information are obtained.

The expected user tag set is used for representing featurescorresponding to a target user group corresponding to theto-be-recommended information. The expected user tag set correspondingto the to-be-recommended information is set in advance. In anembodiment, the expected user tag set may be determined in the followingmanner: obtaining a known user group interested in the to-be-recommendedinformation, and using common user tags of the user group as expecteduser tags, to form the expected user tag set.

In step S1206, a degree of similarity is calculated between the currentuser tag set and the expected user tag set.

In order to determine whether the current user is a target usercorresponding to the to-be-recommended information, after obtaining thecurrent user tag set corresponding to the current user, the servercalculates a degree of similarity between the current user tag set andthe expected user tag set, so as to determine whether the current useris the target user corresponding to the to-be-recommended informationaccording to the degree of similarity. In an embodiment, the degree ofsimilarity between the current user tag set and the expected user tagset may be calculated by calculating an overlapping degree betweencurrent user tags in the current user tag set and expected user tags inthe expected user tag set. For example, there are 20 current user tagsin the current user tag set, there are 25 expected user tags in theexpected user tag set, and there are 15 overlapping user tags betweenthe current user tags and the expected user tags. In this case, anoverlapping degree between the current user tags and the expected usertags is 15/25. The overlapping degree between the current user tags andthe expected user tags may be used as a degree of similarity, that is,the degree of similarity between the current user tag set and theexpected user tag set is ⅗. To calculate the degree of similaritybetween the current user tag set and the expected user tag set moreaccurately, in an embodiment, in addition to obtaining the user tags, itis also necessary to obtain a feature value of each user tag. That is,in addition to calculating the overlapping degree between user tags, itis also necessary to calculate a degree of similarity between identicaluser tags, specifically determine degrees of similarity of tagsaccording to feature values corresponding to the current user tags andfeature values corresponding to the expected user tags, and determine adegree of similarity between the current user tag set and the expecteduser tag set according to the degree of similarity of each user tag.

In step S1208, the to-be-recommended information is recommended to aterminal corresponding to the user identifier according to the degree ofsimilarity.

Specifically, a similarity degree threshold is set in advance. It isdetermined whether the obtained degree of similarity through calculationis greater than the preset similarity degree threshold; if yes, theto-be-recommended information is recommended to the terminalcorresponding to the user identifier; otherwise, the to-be-recommendedinformation is not recommended.

In the foregoing information recommendation method, a current user tagset that can reflect user features is established by using imageinformation; an expected user tag set corresponding to information to berecommended is obtained; and the information is recommended to aterminal corresponding to a user identifier according to a degree ofsimilarity between the current user tag set and the expected user tagset. This method not only improves the accuracy of recommendation, butalso avoids disturbing users who are not interested in the information.

In an embodiment, step S1202 of obtaining image information, the imageinformation having a corresponding user identifier, and generating auser tag set corresponding to the user identifier according to the imageinformation includes: obtaining image information, the image informationhaving a corresponding user identifier, and generating a primary usertag set corresponding to the user identifier according to the imageinformation; and extracting features of the primary user tag set togenerate a corresponding secondary user tag set.

A primary user tag refers to a user tag that can be directly obtainedaccording to the image information. That is, user tags that can beobtained according to information of the image itself are referred to as“primary user tags”, and a tag set formed by the primary user tags isreferred to as a “primary user tag set”. The image information includesimage content information, image acquisition information, image quantityinformation, and the like. All user tags directly obtained according tothe image content information, the image acquisition information, andthe image quantity information are primary user tags. For example, usertags such as scenery, group photo, selfie, and food that are directlyobtained through classification according to the image contentinformation are all primary user tags. A secondary user tag refers to atag indirectly obtained by extracting a feature of a primary user tag.Specifically, a mapping relationship between primary user tags andsecondary user tags are set in advance, where the primary user tags maybe in a many-to-one relationship with the secondary user tags. Acorresponding secondary user tag is determined by extracting featurescorresponding to primary tags. For example, a corresponding secondaryuser tag is determined according to features corresponding to threetags: “baby”, “gender”, and “age”, corresponding to the user. Forexample, it is assumed that a photo quantity (feature) corresponding tothe “baby” tag is 100, a feature corresponding to the gender tag isfemale, and a feature corresponding to the age tag is 25. Then, it canbe inferred that the user is a young mom, and correspondingly, “youngmom” is used as a secondary tag of the user. A social attribute of theuser is extracted according to a group photo quantity (feature)corresponding to the “group photo” tag corresponding to the user. If thegroup photo tag corresponds to a large quantity of photos, it can beinferred that the user is a “sociable” user, and “sociable” is used as asecondary tag of the user. A financial capability of the user can beinferred according to a photographing machine model and a photographinglocation of the user. For example, if the user corresponds to anordinary photographing machine model and an ordinary photographinglocation, it indicates that the user has a normal financial capability,and “normal financial capability” is used as a corresponding secondaryuser tag. FIG. 13 shows a schematic flowchart of obtaining primary usertags from image information and then obtaining secondary user tags fromthe primary user tags in an embodiment.

Step S1206 of calculating a degree of similarity between the currentuser tag set and the expected user tag set includes: calculating adegree of similarity between the secondary user tag set and the expecteduser tag set.

The expected user tags in the expected user tag set may be secondaryuser tags corresponding to the target user group. After obtaining thesecondary user tag set corresponding to the current user, the servercalculates a degree of similarity between the secondary user tag setcorresponding to the current user and the expected user tag set.Specifically, a degree of tag similarity between each secondary user tagand a corresponding expected user tag may be calculated first, and thenthe degree of similarity between the secondary user tag set and theexpected user tag set is calculated according to the degrees of tagsimilarity.

As shown in FIG. 14, in an embodiment, step S1206 of calculating adegree of similarity between the current user tag set and the expecteduser tag set includes the following steps:

In step S1206A, image quantities corresponding to current user tags inthe current user tag set are obtained.

Image quantities corresponding to the current user tags are obtainedfrom the image information corresponding to the current user tag set.The image quantity refers to a quantity of pictures. For example, it isassumed that the user tags include food, scenery, selfie, group photo,and the like. The image quantity corresponding to the food tag is 7; theimage quantity corresponding to the scenery tag is 5; the image quantitycorresponding to the selfie tag is 10; and the image quantitycorresponding to the group photo tag is 5.

In step S1206B, current scores corresponding to the current user tagsare determined according to quantity levels to which the imagequantities belong.

The current score refers to a score that corresponds to a current usertag and that is determined according to an image quantity correspondingto the current user tag. Specifically, a correspondence between imagequantities and scores is set in advance. After obtaining an imagequantity corresponding to a current user tag, the server determines acorresponding current score according to a quantity level to which theimage quantity belongs. For example, a tag scoring system is set. Whenan image quantity corresponding to a user tag is in a range of [1, 10),a current score corresponding to the user tag is set to 1; when theimage quantity is in the range of [10, 50), the current scorecorresponding to the user tag is set to 2; when the image quantity is inthe range of [50, 100), the current score corresponding to the user tagis set to 3; when the image quantity is in the range of [100, 200), thecurrent score corresponding to the user tag is set to 4; when the imagequantity is 200 or more, the current score corresponding to the user tagis set to 5. It is assumed that current user tags of a user A includethe following three tags: food, game, and scenery, where there are 10food images, 5 game images, and 20 scenery images. In this case, currentscores corresponding to the current user tags of the user A are asfollows: a score of 2 corresponding to the food tag, a score of 1corresponding to the game tag, and a score of 2 corresponding to thescenery tag.

In step S1206C, an expected score corresponding to each expected usertag in the expected user tag set is obtained.

The expected score refers to a score corresponding to the expected usertag in the expected user tag set. An expected score corresponding toeach expected user tag in the expected user tag set is set advance. Amanner for setting the expected scores is kept consistent with a mannerof determining the current scores corresponding to the current usertags.

In step S1206D, a degree of similarity between the current user tag setand the expected user tag set through calculation is obtained accordingto the current scores and the expected scores.

The degree of similarity refers to a degree of similarity between acurrent user tag set corresponding to a current user and an expecteduser tag set. Specifically, first, a degree of tag similarity betweeneach current user tag in the current user tag set and an expected usertag in the corresponding expected user tag set is calculated. Then, adegree of similarity between the current user tag set and the expecteduser tag set is calculated through calculation according to the degreeof tag similarity of each user tag. In an embodiment, a degree of tagsimilarity between the current user tag and the expected user tag isdetermined according to a ratio between scores corresponding to thecurrent user tag and the expected user tag, that is, determinedaccording to a ratio between the current score and the expected score. Agreater one in the current score and the standard score is used as adenominator, and a smaller one is used as a numerator. Then, a degree ofsimilarity between the two is determined. For example, if a food tagcorresponds to a current score of 2 and an expected score of 3, 2 isused as a numerator and 3 is used as a denominator to obtain that adegree of similarity between the two is ⅔. A degree of tag similaritybetween each current user tag and each expected user tag can be obtainedthrough calculation in this manner. The calculation of the degree of tagsimilarity is calculation for identical tags. For example, a degree oftag similarity between a “food” tag in the current tag set and a “food”tag in the expected tag set is calculated. After the degree of tagsimilarity corresponding to each user tag is calculated, a degree ofsimilarity between the current user tag set and the expected user tagset can be determined. In an embodiment, a sum of all the degrees of tagsimilarity may be directly used as the degree of similarity between thecurrent user tag set and the expected user tag set.

As shown in FIG. 15, in an embodiment, an information recommendationmethod is proposed. The method includes the following steps:

In step S1501, image information is obtained. The image information hasa corresponding user identifier. A primary user tag set corresponding tothe user identifier is generated according to the image information.

In step S1502, features of the primary user tag set to generate acorresponding secondary user tag set are extracted.

In step S1503, to-be-recommended information and an expected user tagset corresponding to the to-be-recommended information are obtained.

In step S1504, image quantities corresponding to current user tags inthe current user tag set are obtained.

In step S1505, current scores corresponding to current user tags aredetermined according to quantity levels to which the image quantitiesbelong.

In step S1506, an expected score corresponding to each expected user tagin the expected user tag set is obtained.

In step S1507, a degree of similarity between the current user tag setand the expected user tag set through calculation is obtained accordingto the current scores and the expected scores.

In step S1508, the to-be-recommended information is recommended to aterminal corresponding to the user identifier according to the degree ofsimilarity.

It is to be appreciated that, steps in the embodiments of thisapplication are not necessarily performed according to a sequenceindicated by numbers of the steps. Unless otherwise stated clearly inthis specification, performing of the steps is not limited by a strictsequence. The steps may be performed according to other sequences.Moreover, at least some of the steps in the embodiments may includemultiple sub-steps or multiple stages. The sub-steps or stages are notnecessarily performed at the same moment, but can be performed atdifferent moments. The sub-steps or states are not necessarily performedsequentially, but can be performed in turn or alternately with othersteps or at least some of sub-steps or stages of other steps.

As shown in FIG. 16, in an embodiment, an information recommendationapparatus is proposed. The apparatus includes a generating module 1602,a forming module 1604, an output module 1606, and a recommendationmodule 1608. The generating module 1602 is configured to obtain imageinformation, the image information having a corresponding useridentifier, and generate a user tag set corresponding to the useridentifier according to the image information. The forming module 1604is configured to form a feature vector corresponding to the useridentifier according to user tags in the user tag set and thecorresponding image information. The output module 1606 is configured toprocess the feature vector according to a trained informationrecommendation model, to obtain a recommendation parameter ofto-be-recommended information. The recommendation module 1608 isconfigured to recommend the to-be-recommended information to a terminalcorresponding to the user identifier according to the recommendationparameter.

In an embodiment, the image information includes image contentinformation and image acquisition information; and the generating moduleis further configured to: classify images according to the image contentinformation, determine a user tag set corresponding to the contentinformation, and classify the images according to the image acquisitioninformation, and determine a user tag set corresponding to the imageacquisition information.

As shown in FIG. 17, in an embodiment, the forming module 1604 includesa matching module 1604A and a feature vector determining module 1604B.The matching module 1604A is configured to perform matching with presetstandard user models according to the existing user tags in the user tagset and the corresponding image information in a case that a scale ofthe user tag set corresponding to the user identifier is less than apreset scale, and determine a target standard user model matching theuser identifier. The feature vector determining module 1604B isconfigured to obtain a standard user feature vector corresponding to thetarget standard user model, and use the standard user feature vector asthe feature vector corresponding to the user identifier.

In an embodiment, the matching module 1604A is further configured to:calculate degrees of matching between a user corresponding to the useridentifier and the standard user models according to the existing usertags in the user tag set and the corresponding image information, anduse a standard user model with a highest degree of matching obtainedthrough calculation as the target standard user model matching the useridentifier.

In an embodiment, the matching module 1604A is further configured to:obtain a first standard user model from the standard user models as acurrent to-be-matched standard user model; obtain image quantitiescorresponding to the existing user tags in the user tag set; determinecurrent scores corresponding to the existing user tags according toquantity levels to which the image quantities belong; obtain standardscores corresponding to standard user tags that are in the currentto-be-matched standard user model and that are the same as the existinguser tags; obtain degrees of similarity between the existing user tagsin the user tag set and the standard user tags through calculationaccording to the standard scores and the corresponding current scores;obtain a degree of matching between the user corresponding to the useridentifier and the current to-be-matched standard user model accordingto the degrees of similarity; and obtain a next standard user model asthe current to-be-matched standard user model, and repeatedly performthe step of obtaining standard scores corresponding to standard usertags that are in the current to-be-matched standard user model and thatare the same as the existing user tags, until the degrees of matchingbetween the user and the standard user models are obtained.

As shown in FIG. 18, in an embodiment, the information recommendationapparatus further includes a training user tag set generating module1610, a training feature vector forming module 1612, a standard outputresult obtaining module 1614, and a training module 1616. The traininguser tag set generating module 1610 is configured to obtain trainingimage information, and generate a training user tag set according to thetraining image information. The training feature vector forming module1612 is configured to form a training feature vector according totraining user tags in the training user tag set and the correspondingimage information. The standard output result obtaining module 1614 isconfigured to obtain a standard output result corresponding to thetraining feature vector. The training module 1616 is configured toperform model training by using the training feature vector and thecorresponding standard output result as a training sample, to obtain atarget information recommendation model.

In an embodiment, the generating module is further configured to:determine a primary user tag set corresponding to the user identifieraccording to the image information; extract features of the primary usertag set to generate a corresponding secondary user tag set; and form theuser tag set corresponding to the user identifier according to theprimary user tag set and the secondary user tag set.

In an embodiment, there are multiple pieces of to-be-recommendedinformation, and each piece of to-be-recommended information has acorresponding information recommendation model; the output module isfurther configured to: input the feature vector into the trainedinformation recommendation models, and output a correspondingrecommendation parameter set, each recommendation parameter in therecommendation parameter set being used for determining a recommendationprobability of each piece of to-be-recommended information; generate aninformation recommendation list corresponding to the user identifieraccording to the recommendation probability corresponding to each pieceof to-be-recommended information; and determine target to-be-recommendedinformation corresponding to the user identifier according to theinformation recommendation list.

In an embodiment, the recommendation module is further configured topush the to-be-recommended information to the terminal corresponding tothe user identifier in a form of a picture if the recommendationparameter is greater than a preset threshold.

As shown in FIG. 19, in an embodiment, an information recommendationapparatus is proposed. The apparatus includes a current user tag setgenerating module 1902, an expected user tag set obtaining module 1904,a similarity degree calculation module 1906, and an informationrecommendation module 1908. The current user tag set generating module1902 is configured to obtain image information, the image informationhaving a corresponding user identifier, and generate a current user tagset corresponding to the user identifier according to the imageinformation. The expected user tag set obtaining module 1904 isconfigured to obtain to-be-recommended information, and obtain anexpected user tag set corresponding to the to-be-recommendedinformation. The similarity degree calculation module 1906 is configuredto calculate a degree of similarity between the current user tag set andthe expected user tag set. The information recommendation module 1908 isconfigured to recommend the to-be-recommended information to a terminalcorresponding to the user identifier according to the degree ofsimilarity.

In an embodiment, the current user tag set generating module is furtherconfigured to: obtain image information, the image information having acorresponding user identifier, generate a primary user tag setcorresponding to the user identifier according to the image information,and extract features of the primary user tag set to generate acorresponding secondary user tag set; the similarity degree calculationmodule is further configured to calculate a degree of similarity betweenthe secondary user tag set and the expected user tag set.

In an embodiment, the similarity degree calculation module is furtherconfigured to: obtain image quantities corresponding to current usertags in the current user tag set; determine current scores correspondingto the current user tags according to quantity levels to which the imagequantities belong; obtain an expected score corresponding to each usertag in the expected user tag set; and obtain a degree of similaritybetween the current user tag set and the expected user tag set throughcalculation according to the current scores and the expected scores.

FIG. 20 is a schematic diagram of an internal structure of a computerdevice in an embodiment. Referring to FIG. 20, the computer deviceincludes a processor (or processing circuitry), a memory, and a networkinterface that are connected through a system bus. The memory includes anon-volatile storage medium and an internal memory. The non-volatilestorage medium of the computer device may include an operating systemand computer readable instructions. When the computer readableinstructions are executed, the processor may be enabled to perform aninformation recommendation method. The processor of the computer deviceis configured to provide computing and control capabilities, to supportrunning of the whole computer device. The internal memory storescomputer readable instructions. When the computer readable instructionsare executed by the processor, the processor may be enabled to performan information recommendation method. The network interface of thecomputer device is used for network communication. A person skilled inthe art may understand that, the structure shown in FIG. 20 is merely ablock diagram of a partial structure related to the solution of thisapplication, and but does not limit the computer device to which thesolution of this application is applied. A specific computer device mayinclude components more or fewer than those shown in the figure, or somecomponents may be combined, or a different component arrangement may beused.

In an embodiment, a computer device is further provided. An internalstructure of the computer device may be as shown in FIG. 20. Thecomputer device includes an information recommendation apparatus. Theinformation recommendation apparatus includes modules, and each modulemay be completely or partially implemented by software, hardware, or acombination thereof.

In an embodiment, the information recommendation apparatus provided inthis application may be implemented in a form of computer readableinstructions, which is stored in a non-transitory computer-readablemedium for example. The computer readable instructions may be run on thecomputer device shown in FIG. 20. The non-volatile storage medium of thecomputer device may store program modules forming the informationrecommendation apparatus, for example, the obtaining module 1602, theforming module 1604, the output module 1606, and the recommendationmodule 1608 in FIG. 16. The program modules include computer readableinstructions. The computer readable instructions are used for enablingthe computer device to perform steps in the information recommendationmethods in the embodiments of this application described in thisspecification. The processor in the computer device can invoke theprogram modules of the information recommendation apparatus stored inthe non-volatile storage medium of the computer device and run thecorresponding readable instructions, so as to implement functionscorresponding to the modules of the information recommendation apparatusin this specification. The program modules include computer readableinstructions. The computer readable instructions are used for enablingthe computer device to perform steps in the information recommendationmethods in the embodiments of this application described in thisspecification. The processor in the computer device can invoke theprogram modules of the information recommendation apparatus stored inthe non-volatile storage medium of the computer device and run thecorresponding readable instructions, so as to implement functionscorresponding to the modules of the information recommendation apparatusin this specification. For example, the computer device may obtain imageinformation through the generating module 1602 in the informationrecommendation apparatus shown in FIG. 16, the image information havinga corresponding user identifier, and generate a user tag setcorresponding to the user identifier according to the image information;form, through the forming module 1604, a feature vector corresponding tothe user identifier according to user tags in the user tag set and thecorresponding image information; process, through the output module1606, the feature vector according to a trained informationrecommendation model, to obtain a recommendation parameter ofto-be-recommended information; and recommend, through the recommendationmodule 1608, the to-be-recommended information to a terminalcorresponding to the user identifier according to the recommendationparameter.

In an embodiment, the information recommendation apparatus provided inthis application may be implemented in a form of computer readableinstructions, which is stored in a non-transitory computer-readablemedium for example. The computer readable instructions may be run on theshown in FIG. 20. The non-volatile storage medium of the computer devicemay store program modules forming the information recommendationapparatus, for example, the current user tag set generating module 1902,the expected user tag set obtaining module 1904, the similarity degreecalculation module 1906, and the information recommendation module 1908.The program modules include computer readable instructions. The computerreadable instructions are used for enabling the computer device toperform steps in the information recommendation methods in theembodiments of this application described in this specification. Theprocessor in the computer device can invoke the program modules of theinformation recommendation apparatus stored in the non-volatile storagemedium of the computer device and run the corresponding readableinstructions, so as to implement functions corresponding to the modulesof the information recommendation apparatus in this specification. Theprogram modules include computer readable instructions. The computerreadable instructions are used for enabling the computer device toperform steps in the information recommendation methods in theembodiments of this application described in this specification. Theprocessor in the computer device can invoke the program modules of theinformation recommendation apparatus stored in the non-volatile storagemedium of the computer device and run the corresponding readableinstructions, so as to implement functions corresponding to the modulesof the information recommendation apparatus in this specification.

For example, the computer device may obtain image information throughthe current user tag set generating module 1902 in the informationrecommendation apparatus shown in FIG. 19, the image information havinga corresponding user identifier, and generate a current user tag setcorresponding to the user identifier according to the image information;obtain, through the expected user tag set obtaining module 1904,to-be-recommended information, and obtain an expected user tag setcorresponding to the to-be-recommended information; calculate, throughthe similarity degree calculation module 1906, a degree of similaritybetween the current user tag set and the expected user tag set; andrecommend, through the information recommendation module 1908, theto-be-recommended information to a terminal corresponding to the useridentifier according to the degree of similarity.

A person of ordinary skill in the art may understand that all or some ofthe processes in the foregoing method embodiments may be implemented bycomputer readable instructions instructing relevant hardware. Thecomputer readable instructions may be stored in a non-volatile ornon-transitory computer readable storage medium. When the computerreadable instructions are executed, the processes of the embodiments ofthe foregoing methods may be included. In the embodiments of thisapplication, any reference to a memory, storage, a database, or othermedia may include a non-volatile and/or volatile memory. Thenon-volatile memory may include a read-only memory (ROM), a programmableROM (PROM), an electrically programmable ROM (EPROM), an electricallyerasable programmable ROM (EEPROM), or a flash memory. The volatilememory may include a random access memory (RAM) or an external cachememory. As an illustration rather than a limitation, the RAM isavailable in many forms, for example, a static RAM (SRAM), a dynamic RAM(DRAM), a synchronous DRAM (SDRAM), a double data rate SDRAM (DDRSDRAM),an enhanced SDRAM (ESDRAM), a synchronous link DRAM (SLDRAM), a RambusDirect RAM (RDRAM), a Direct Rambus Dynamic RAM (DRDRAM).

The foregoing embodiments only represent several implementations of thisapplication, which are specifically described in detail, but cannot beconstrued as a limitation on the patent scope of this application. For aperson of ordinary skill in the art, several modifications andimprovements can be made without departing from the conception of thisapplication. All such modifications and improvements belong to theprotection scope of this application. Therefore, the patent protectionscope of this application is to be subject to the appended claims.

What is claimed is:
 1. An information recommendation method, comprising: obtaining, by a processing circuitry, image information corresponding to an image, the image is associated with a user identifier; generating, by the processing circuitry, a user tag set corresponding to the user identifier and the image information; forming, by the processing circuitry, a feature vector corresponding to user tags in the user tag set and the image information; processing, by the processing circuitry, the feature vector according to a trained information recommendation model, to obtain a recommendation parameter of to-be-recommended information; and providing, by the processing circuitry, a recommendation of the to-be-recommended information to a terminal corresponding to the user identifier according to the recommendation parameter.
 2. The method according to claim 1, wherein the image information includes image content information and image acquisition information, the image content information including a plurality of images; and the generating includes classifying, by the processing circuitry, the images according to the image content information, and determining a first user tag set corresponding to the image content information based on a result of the classification according to the image content information; and classifying, by the processing circuitry, the images according to the image acquisition information, and determining a second user tag set corresponding to the image acquisition information based on a result of the classification according to the image acquisition information.
 3. The method according to claim 1, wherein the forming comprises: performing, by the processing circuitry, matching with standard user models according to the user tags in the user tag set and the image information corresponding to the user tags in a case that a scale of the user tag set corresponding to the user identifier is less than a preset scale; determining, by the processing circuitry, a target standard user model of the standard user models matching the user identifier; and obtaining, by the processing circuitry, a standard user feature vector corresponding to the target standard user model as the feature vector corresponding to the user identifier.
 4. The method according to claim 3, wherein the performing comprises: calculating, by the processing circuitry, degrees of matching between a user corresponding to the user identifier and the standard user models according to the user tags in the user tag set and the image information corresponding to the user tags; and selecting, by the processing circuitry, a standard user model of the standard user models with a highest degree of matching of the calculated degrees of matching as the target standard user model matching the user identifier.
 5. The method according to claim 4, wherein the calculating comprises: obtaining, by the processing circuitry, image quantities corresponding to the user tags in the user tag set; determining, by the processing circuitry, current scores corresponding to the user tags according to the image quantities; for each of the standard user models, obtaining, by the processing circuitry, standard scores corresponding to standard user tags that are in the respective standard user model and that are the same as the user tags, calculating, by the processing circuitry, degrees of similarity between the user tags in the user tag set and the standard user tags according to the standard scores and the corresponding current scores, and obtaining, by the processing circuitry, the degree of matching between the user corresponding to the user identifier and the respective standard user model according to the degrees of similarity.
 6. The method according to claim 1, further comprising: obtaining, by the processing circuitry, training image information; generating, by the processing circuitry, a training user tag set according to the training image information; forming, by the processing circuitry, a training feature vector according to training user tags in the training user tag set and the training image information corresponding to the training user tag set; obtaining, by the processing circuitry, a standard output result corresponding to the training feature vector; and performing, by the processing circuitry, model training by using the training feature vector and the standard output result as a training sample, to obtain a target information recommendation model.
 7. The method according to claim 1, wherein the generating comprises: determining, by the processing circuitry, a primary user tag set corresponding to the user identifier and the image information; generating, by the processing circuitry, a secondary user tag set based on extracted features of the primary user tag set; and forming, by the processing circuitry, the user tag set corresponding to the user identifier according to the primary user tag set and the secondary user tag set.
 8. The method according to claim 1, wherein each piece of the to-be-recommended information has a corresponding information recommendation model; and the processing includes processing, by the processing circuitry, the feature vector according to the corresponding information recommendation models, to obtain a corresponding recommendation parameter set, each recommendation parameter in the recommendation parameter set being used for determining a recommendation probability of one piece of the to-be-recommended information, generating, by the processing circuitry, an information recommendation list corresponding to the user identifier according to the recommendation probabilities corresponding to the pieces of the to-be-recommended information, and determining, by the processing circuitry, target to-be-recommended information corresponding to the user identifier according to the information recommendation list.
 9. The method according to claim 1, wherein the providing comprises: providing, by the processing circuitry, the to-be-recommended information to the terminal corresponding to the user identifier in a form of a picture when the recommendation parameter is greater than a preset threshold.
 10. An information recommendation method, comprising: obtaining, by processing circuitry, image information, the image information corresponding to an image, the image is associated with a user identifier; generating, by the processing circuitry, a current user tag set corresponding to the user identifier and the image information; obtaining, by the processing circuitry, to-be-recommended information; obtaining, by the processing circuitry, an expected user tag set corresponding to the to-be-recommended information; calculating, by the processing circuitry, a degree of similarity between the current user tag set and the expected user tag set; and providing, by the processing circuitry, a recommendation of the to-be-recommended information to a terminal corresponding to the user identifier according to the degree of similarity.
 11. The method according to claim 10, wherein the generating includes generating, by the processing circuitry, a primary user tag set corresponding to the user identifier and the image information, generating, by the processing circuitry, a secondary user tag set based on extracted features of the primary user tag set, and generating the current user tag set based on the primary user tag set and the secondary user tag set; and the calculating includes calculating, by the processing circuitry, the degree of similarity between the secondary user tag set and the expected user tag set.
 12. The method according to claim 10, wherein the calculating comprises: obtaining, by the processing circuitry, image quantities corresponding to current user tags in the current user tag set; determining, by the processing circuitry, current scores corresponding to the current user tags according to the image quantities; obtaining, by the processing circuitry, an expected score corresponding to each user tag in the expected user tag set; and calculating, by the processing circuitry, the degree of similarity between the current user tag set and the expected user tag set according to the current scores and the expected scores.
 13. An information processing apparatus, comprising: processing circuitry configured to obtain image information corresponding to an image, the image is associated with a user identifier; generating a user tag set corresponding to the user identifier and the image information; forming a feature vector corresponding to user tags in the user tag set and the image information; processing the feature vector according to a trained information recommendation model, to obtain a recommendation parameter of to-be-recommended information; and providing a recommendation of the to-be-recommended information to a terminal corresponding to the user identifier according to the recommendation parameter.
 14. The information processing apparatus according to claim 13, wherein the image information includes image content information and image acquisition information, the image content information including a plurality of images; and the processing circuitry is configured to classify the images according to the image content information, and determine a first user tag set corresponding to the image content information based on a result of the classification according to the image content information; and classify the images according to the image acquisition information, and determine a second user tag set corresponding to the image acquisition information based on a result of the classification according to the image acquisition information.
 15. The information processing apparatus according to claim 13, wherein the processing circuitry is configured to perform matching with standard user models according to the user tags in the user tag set and the image information corresponding to the user tags in a case that a scale of the user tag set corresponding to the user identifier is less than a preset scale; determine a target standard user model of the standard user models matching the user identifier; and obtain a standard user feature vector corresponding to the target standard user model as the feature vector corresponding to the user identifier.
 16. The information processing apparatus according to claim 15, wherein the processing circuitry is configured to calculate degrees of matching between a user corresponding to the user identifier and the standard user models according to the user tags in the user tag set and the image information corresponding to the user tags; and select a standard user model of the standard user models with a highest degree of matching of the calculated degrees of matching as the target standard user model matching the user identifier.
 17. The information processing apparatus according to claim 16, wherein the processing circuitry is configured to obtain image quantities corresponding to the user tags in the user tag set; determine current scores corresponding to the user tags according to the image quantities; and for each of the standard user models, obtain standard scores corresponding to standard user tags that are in the respective standard user model and that are the same as the user tags, calculate degrees of similarity between the user tags in the user tag set and the standard user tags according to the standard scores and the corresponding current scores, and obtain the degree of matching between the user corresponding to the user identifier and the respective standard user model according to the degrees of similarity.
 18. The information processing apparatus according to claim 13, wherein the processing circuitry is configured to obtain training image information; generate a training user tag set according to the training image information; form a training feature vector according to training user tags in the training user tag set and the training image information corresponding to the training user tag set; obtain a standard output result corresponding to the training feature vector; and perform model training by using the training feature vector and the standard output result as a training sample, to obtain a target information recommendation model.
 19. The information processing apparatus according to claim 13, wherein the processing circuitry is configured to determine a primary user tag set corresponding to the user identifier and the image information; extract features of the primary user tag set to generate a corresponding secondary user tag set; and form the user tag set corresponding to the user identifier according to the primary user tag set and the secondary user tag set.
 20. An information processing apparatus, comprising: processing circuitry configured to obtain image information, the image information corresponding to an image, the image is associated with a user identifier; generate a current user tag set corresponding to the user identifier and the image information; obtain to-be-recommended information; obtain an expected user tag set corresponding to the to-be-recommended information; calculate a degree of similarity between the current user tag set and the expected user tag set; and provide a recommendation of the to-be-recommended information to a terminal corresponding to the user identifier according to the degree of similarity. 